{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:30:24Z","timestamp":1757619024036,"version":"3.44.0"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031986871"},{"type":"electronic","value":"9783031986888"}],"license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-031-98688-8_1","type":"book-chapter","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T09:08:56Z","timestamp":1752656936000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transductive Survival Ranking for Pan-Cancer Automatic Risk Stratification Using Whole Slide Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2116-5819","authenticated-orcid":false,"given":"Ethar","family":"Alzaid","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9129-1189","authenticated-orcid":false,"given":"Fayyaz","family":"Minhas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"issue":"4","key":"1_CR1","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1007\/s10278-020-00351-z","volume":"33","author":"N Kumar","year":"2020","unstructured":"Kumar, N., Gupta, R., Gupta, S.: Whole slide imaging (WSI) in pathology: current perspectives and future directions. J. Digit. Imaging 33(4), 1034 (2020). https:\/\/doi.org\/10.1007\/s10278-020-00351-z","journal-title":"J. Digit. Imaging"},{"issue":"9","key":"1_CR2","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1080\/00949655.2022.2139379","volume":"93","author":"M Yu","year":"2023","unstructured":"Yu, M., Zhao, W., Zhou, Y., Wu, C.: Robust online detection on highly censored data using a semi-parametric EWMA chart. J. Stat. Comput. Simul. 93(9), 1403\u20131419 (2023). https:\/\/doi.org\/10.1080\/00949655.2022.2139379","journal-title":"J. Stat. Comput. Simul."},{"issue":"2","key":"1_CR3","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox, D.R.: Regression models and life-tables. J. R. Stat. Soc. Ser. B Methodol. 34(2), 187\u2013202 (1972). https:\/\/doi.org\/10.1111\/j.2517-6161.1972.tb00899.x","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"issue":"1","key":"1_CR4","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 24 (2018). https:\/\/doi.org\/10.1186\/s12874-018-0482-1","journal-title":"BMC Med. Res. Methodol."},{"key":"1_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/873595","volume":"2013","author":"Y Chen","year":"2013","unstructured":"Chen, Y., Jia, Z., Mercola, D., Xie, X.: A gradient boosting algorithm for survival analysis via direct optimization of concordance index. Comput. Math. Methods Med. 2013, e873595 (2013). https:\/\/doi.org\/10.1155\/2013\/873595","journal-title":"Comput. Math. Methods Med."},{"issue":"1","key":"1_CR6","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1186\/s12874-021-01375-x","volume":"21","author":"KL Pickett","year":"2021","unstructured":"Pickett, K.L., Suresh, K., Campbell, K.R., Davis, S., Juarez-Colunga, E.: Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker. BMC Med. Res. Methodol. 21(1), 216 (2021). https:\/\/doi.org\/10.1186\/s12874-021-01375-x","journal-title":"BMC Med. Res. Methodol."},{"issue":"4","key":"1_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006076","volume":"14","author":"T Ching","year":"2018","unstructured":"Ching, T., Zhu, X., Garmire, L.X.: Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLOS Comput. Biol. 14(4), e1006076 (2018). https:\/\/doi.org\/10.1371\/journal.pcbi.1006076","journal-title":"PLOS Comput. Biol."},{"key":"1_CR8","doi-asserted-by":"publisher","unstructured":"Zhu, X., Yao, J., Zhu, F., Huang, J.: WSISA: making survival prediction from whole slide histopathological images. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 6855\u20136863. IEEE, Honolulu (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.725","DOI":"10.1109\/CVPR.2017.725"},{"key":"1_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103252","volume":"97","author":"Z Wang","year":"2024","unstructured":"Wang, Z., et al.: Dual-stream multi-dependency graph neural network enables precise cancer survival analysis. Med. Image Anal. 97, 103252 (2024). https:\/\/doi.org\/10.1016\/j.media.2024.103252","journal-title":"Med. Image Anal."},{"issue":"1","key":"1_CR10","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/JBHI.2023.3307584","volume":"28","author":"R Yan","year":"2024","unstructured":"Yan, R., Lv, Z., Yang, Z., Lin, S., Zheng, C., Zhang, F.: Sparse and hierarchical transformer for survival analysis on whole slide images. IEEE J. Biomed. Health Inform. 28(1), 7\u201318 (2024)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"1_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107733","volume":"241","author":"Z Wang","year":"2023","unstructured":"Wang, Z., et al.: Surformer: an interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images. Comput. Methods Programs Biomed. 241, 107733 (2023). https:\/\/doi.org\/10.1016\/j.cmpb.2023.107733","journal-title":"Comput. Methods Programs Biomed."},{"issue":"1","key":"1_CR12","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1186\/1471-2288-12-102","volume":"12","author":"H-C Chen","year":"2012","unstructured":"Chen, H.-C., Kodell, R.L., Cheng, K.F., Chen, J.J.: Assessment of performance of survival prediction models for cancer prognosis. BMC Med. Res. Methodol. 12(1), 102 (2012). https:\/\/doi.org\/10.1186\/1471-2288-12-102","journal-title":"BMC Med. Res. Methodol."},{"issue":"62","key":"1_CR13","first-page":"1687","volume":"7","author":"R Collobert","year":"2006","unstructured":"Collobert, R., Sinz, F., Weston, J., Bottou, L.: Large scale transductive SVMs. J. Mach. Learn. Res. 7(62), 1687\u20131712 (2006)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"1_CR14","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.cell.2018.02.052","volume":"173","author":"J Liu","year":"2018","unstructured":"Liu, J., et al.: An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173(2), 400-416.e11 (2018). https:\/\/doi.org\/10.1016\/j.cell.2018.02.052","journal-title":"Cell"},{"key":"1_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102302","volume":"110","author":"Z Tang","year":"2023","unstructured":"Tang, Z., et al.: Explainable survival analysis with uncertainty using convolution-involved vision transformer. Comput. Med. Imaging Graph. 110, 102302 (2023). https:\/\/doi.org\/10.1016\/j.compmedimag.2023.102302","journal-title":"Comput. Med. Imaging Graph."},{"key":"1_CR16","doi-asserted-by":"publisher","unstructured":"Shaikovski, G., et al.: PRISM: a multi-modal generative foundation model for slide-level histopathology (2024). arXiv: arXiv:2405.10254. https:\/\/doi.org\/10.48550\/arXiv.2405.10254","DOI":"10.48550\/arXiv.2405.10254"},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Ding, T., et al.: Multimodal whole slide foundation model for pathology (2024). arXiv: arXiv:2411.19666. https:\/\/doi.org\/10.48550\/arXiv.2411.19666","DOI":"10.48550\/arXiv.2411.19666"},{"key":"1_CR18","unstructured":"Alzaid, E., Dawood, M., Minhas, F.: A transductive approach to survival ranking for cancer risk stratification. In: Proceedings of the 18th Machine Learning in Computational Biology meeting, pp. 101\u2013109. PMLR (2024). https:\/\/proceedings.mlr.press\/v240\/alzaid24a.html. Accessed 27 Mar 2024"},{"key":"1_CR19","doi-asserted-by":"publisher","unstructured":"Huang, J.: Transductive transfer learning for visual recognition (2023), https:\/\/doi.org\/10.32657\/10356\/164573","DOI":"10.32657\/10356\/164573"},{"key":"1_CR20","doi-asserted-by":"publisher","unstructured":"Shu, L., Latecki, L.J.: Transductive domain adaptation with affinity learning. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM \u201915, pp. 1903\u20131906. Association for Computing Machinery, New York (2015). https:\/\/doi.org\/10.1145\/2806416.2806643","DOI":"10.1145\/2806416.2806643"},{"key":"1_CR21","doi-asserted-by":"publisher","unstructured":"Vapnik, V.N.: The vicinal risk minimization principle and the SVMs. In: Vapnik, V.N. (ed.) The Nature of Statistical Learning Theory. Statistics for Engineering and Information Science, pp. 267\u2013290. Springer, New York (2000). https:\/\/doi.org\/10.1007\/978-1-4757-3264-1_9","DOI":"10.1007\/978-1-4757-3264-1_9"},{"key":"1_CR22","doi-asserted-by":"publisher","unstructured":"Ng, A.Y.: Feature selection, L1 vs. L2 regularization, and rotational invariance. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML \u201904 , p. 78. Association for Computing Machinery, New York (2004). https:\/\/doi.org\/10.1145\/1015330.1015435","DOI":"10.1145\/1015330.1015435"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Minhas, F., Toss, M.S., ul Wahab, N., Rakha, E., Rajpoot, N.M.: L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy na\u00efve breast cancer patients. In: Kamp, M., et al. (eds.) Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 390\u2013400. Springer, Cham (2021)","DOI":"10.1007\/978-3-030-93733-1_27"},{"key":"1_CR24","doi-asserted-by":"publisher","first-page":"23","DOI":"10.4137\/CPath.S31563","volume":"8","author":"J Makki","year":"2015","unstructured":"Makki, J.: Diversity of breast carcinoma: histological subtypes and clinical relevance. Clin. Med. Insights Pathol. 8, 23 (2015). https:\/\/doi.org\/10.4137\/CPath.S31563","journal-title":"Clin. Med. Insights Pathol."},{"issue":"1","key":"1_CR25","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.bbcan.2018.06.003","volume":"1870","author":"DJ Sanchez","year":"2018","unstructured":"Sanchez, D.J., Simon, M.C.: Genetic and metabolic hallmarks of clear cell renal cell carcinoma. Biochim. Biophys. Acta Rev. Cancer 1870(1), 23 (2018). https:\/\/doi.org\/10.1016\/j.bbcan.2018.06.003","journal-title":"Biochim. Biophys. Acta Rev. Cancer"},{"issue":"2","key":"1_CR26","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/sim.4780030207","volume":"3","author":"FE Harrell","year":"1984","unstructured":"Harrell, F.E., Lee, K.L., Califf, R.M., Pryor, D.B., Rosati, R.A.: Regression modelling strategies for improved prognostic prediction. Stat. Med. 3(2), 143\u2013152 (1984). https:\/\/doi.org\/10.1002\/sim.4780030207","journal-title":"Stat. Med."},{"issue":"7447","key":"1_CR27","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1136\/bmj.328.7447.1073","volume":"328","author":"JM Bland","year":"2004","unstructured":"Bland, J.M., Altman, D.G.: The logrank test. BMJ 328(7447), 1073 (2004). https:\/\/doi.org\/10.1136\/bmj.328.7447.1073","journal-title":"BMJ"},{"key":"1_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.spl.2020.108865","volume":"166","author":"CJ DiCiccio","year":"2020","unstructured":"DiCiccio, C.J., DiCiccio, T.J., Romano, J.P.: Exact tests via multiple data splitting. Stat. Probab. Lett. 166, 108865 (2020). https:\/\/doi.org\/10.1016\/j.spl.2020.108865","journal-title":"Stat. Probab. Lett."},{"issue":"282","key":"1_CR29","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1080\/01621459.1958.10501452","volume":"53","author":"EL Kaplan","year":"1958","unstructured":"Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457\u2013481 (1958). https:\/\/doi.org\/10.1080\/01621459.1958.10501452","journal-title":"J. Am. Stat. Assoc."},{"key":"1_CR30","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1186\/1471-2288-12-102","volume":"12","author":"H-C Chen","year":"2012","unstructured":"Chen, H.-C., Kodell, R.L., Cheng, K.F., Chen, J.J.: Assessment of performance of survival prediction models for cancer prognosis. BMC Med. Res. Methodol. 12, 102 (2012). https:\/\/doi.org\/10.1186\/1471-2288-12-102","journal-title":"BMC Med. Res. Methodol."},{"key":"1_CR31","unstructured":"Meseeha, M., Attia, M.: Colon polyps. In: StatPearls. StatPearls Publishing, Treasure Island (2025). http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK430761\/. Accessed 26 Mar 2025"},{"key":"1_CR32","unstructured":"Adenocarcinoma. https:\/\/www.pathologyoutlines.com\/topic\/colontumoradenocarcinoma.html. Accessed 26 Mar 2025"},{"issue":"10","key":"1_CR33","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1038\/s41580-023-00599-7","volume":"24","author":"O Bell","year":"2023","unstructured":"Bell, O., Burton, A., Dean, C., Gasser, S.M., Torres-Padilla, M.-E.: Heterochromatin definition and function. Nat. Rev. Mol. Cell Biol. 24(10), 691\u2013694 (2023). https:\/\/doi.org\/10.1038\/s41580-023-00599-7","journal-title":"Nat. Rev. Mol. Cell Biol."},{"issue":"8","key":"1_CR34","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1046\/j.1440-1827.2003.01514.x","volume":"53","author":"H Yagata","year":"2003","unstructured":"Yagata, H., et al.: Comedonecrosis is an unfavorable marker in node-negative invasive breast carcinoma. Pathol. Int. 53(8), 501\u2013506 (2003). https:\/\/doi.org\/10.1046\/j.1440-1827.2003.01514.x","journal-title":"Pathol. Int."},{"key":"1_CR35","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1186\/s40001-023-01581-x","volume":"28","author":"Z Wen","year":"2023","unstructured":"Wen, Z., et al.: Risk factors analysis and survival prediction model establishment of patients with lung adenocarcinoma based on different pyroptosis-related gene subtypes. Eur. J. Med. Res. 28, 601 (2023). https:\/\/doi.org\/10.1186\/s40001-023-01581-x","journal-title":"Eur. J. Med. Res."},{"issue":"3","key":"1_CR36","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s10462-023-10681-3","volume":"57","author":"S Wiegrebe","year":"2024","unstructured":"Wiegrebe, S., Kopper, P., Sonabend, R., Bischl, B., Bender, A.: Deep learning for survival analysis: a review. Artif. Intell. Rev. 57(3), 65 (2024). https:\/\/doi.org\/10.1007\/s10462-023-10681-3","journal-title":"Artif. Intell. Rev."}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-98688-8_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T11:47:02Z","timestamp":1757245622000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-98688-8_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"ISBN":["9783031986871","9783031986888"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-98688-8_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,7,17]]},"assertion":[{"value":"17 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leeds","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.leeds.ac.uk\/miua\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}